PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf

• URL: http://arxiv.org/abs/2606.08857v1
• Date: Sun, 07 Jun 2026 22:17:15 GMT
• Title: PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf
• Abstract summary: We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments.<n>PaperMentor integrates an expert skill library carefully curated from established researchers' writing advice with 12 specialized agents covering different aspects of paper writing.<n>In a user study, 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline uswithout the skill library.
• Score: 51.79108552674703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. PaperMentor integrates an expert skill library carefully curated from established researchers' writing advice with 12 specialized agents covering different aspects of paper writing, such as formatting compliance, phrasing accuracy, and terminology consistency. In a user study (n=14), 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline uswithout the skill library. We release PaperMentor as open source for public use. Our code is publicly available under the AGPL-3.0 license at https://github.com/jiarui-liu/overleaf

Related papers

• PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress [130.58520199343707]
  PaperDoctor is an agent framework for pre-submission feedback with three key innovations.<n>First, a holistic hierarchical framework evaluates writing, layout, references, code, theory, prior work, and experiments.<n>Second, each finding contains an observation, a pointer to specific evidence such as a sentence, equation, or code line, and a revision suggestion.<n>Third, PaperDoctor selectively rebuilds and reruns experiments based on claim importance and compute budget.
  arXiv  Detail & Related papers  (2026-09-15T11:08:11Z)
• From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent [52.42081442204093]
  We argue that a key limitation is the lack of flexibility to proactively investigate suspicious parts of a paper.<n>We propose ProReviewer, a scientific peer review agent that proactively reviews a paper guided by a maintained, structured review log.<n> Experiments show that ProReviewer with an 8B backbone, trained by supervised fine-tuning and optimized by reinforcement learning, achieves the highest average score across five quality dimensions.
  arXiv  Detail & Related papers  (2026-06-11T13:38:23Z)
• When AI reviews science: Can we trust the referee? [73.47745294608072]
  We map attacks across the review lifecycle -- training and data retrieval, desk review, deep review, rebuttal, and system-level.<n>We use two advanced LLM-based referees to isolate the causal effects of prestige framing, assertion strength, rebuttal sycophancy, and contextual poisoning on review scores.
  arXiv  Detail & Related papers  (2026-04-26T08:03:32Z)
• APRES: An Agentic Paper Revision and Evaluation System [44.44345338738518]
  The primary way scientists communicate their work and receive feedback from the community is through peer review.<n>In this paper, we introduce a novel method APRES powered by Large Language Models (LLMs) to update a scientific papers text based on an evaluation rubric.<n>Our automated method discovers a rubric that is highly predictive of future citation counts, and integrate it with APRES in an automated system that revises papers to enhance their quality and impact.
  arXiv  Detail & Related papers  (2026-03-03T16:29:13Z)
• Exposía: Academic Writing Assessment of Exposés and Peer Feedback [56.428320613219306]
  We present Exposa, the first public dataset that connects writing and feedback assessment in higher education.<n>We use Exposa to benchmark state-of-the-art open-source large language models (LLMs) for two tasks: automated scoring of (1) the proposals and (2) the student reviews.
  arXiv  Detail & Related papers  (2026-01-10T11:33:26Z)
• SEAGraph: Unveiling the Whole Story of Paper Review Comments [23.254167273244416]
  In the traditional peer review process, authors often receive vague or insufficiently detailed feedback.<n>This raises the critical question of how to enhance authors' comprehension of review comments.<n>We present SEAGraph, a novel framework developed to clarify review comments by uncovering the underlying intentions.
  arXiv  Detail & Related papers  (2024-12-16T16:24:36Z)
• Automated Focused Feedback Generation for Scientific Writing Assistance [6.559560602099439]
  SWIF$2$T: a Scientific WrIting Focused Feedback Tool. It is designed to generate specific, actionable and coherent comments, which identify weaknesses in a scientific paper and/or propose revisions to it. We compile a dataset of 300 peer reviews citing weaknesses in scientific papers and conduct human evaluation. The results demonstrate the superiority in specificity, reading comprehension, and overall helpfulness of SWIF$2$T's feedback compared to other approaches.
  arXiv  Detail & Related papers  (2024-05-30T20:56:41Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [51.26815896167173]
  We present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions.<n>Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices.<n>Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• PaperCard for Reporting Machine Assistance in Academic Writing [48.33722012818687]
  ChatGPT, a question-answering system released by OpenAI in November 2022, has demonstrated a range of capabilities that could be utilised in producing academic papers. This raises critical questions surrounding the concept of authorship in academia. We propose a framework we name "PaperCard", a documentation for human authors to transparently declare the use of AI in their writing process.
  arXiv  Detail & Related papers  (2023-10-07T14:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.